






"""simple docstring"""
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


snake_case : Dict     		=		1_6
snake_case : List[Any]     		=		3_2


def 							A						(				__snake_case: Accelerator ,     __snake_case: int = 1_6  )    ->		Union[str, Any]:

       """simple docstring"""





       __magic_name__      				=    AutoTokenizer.from_pretrained('bert-base-cased'  )
       __magic_name__      				=    load_dataset('glue' ,     'mrpc'  )

       def tokenize_function(__snake_case: Any  ):
              # max_length=None => use the model max length (it's actually the default)
              __magic_name__      				=    tokenizer(examples['sentence1'] ,     examples['sentence2'] ,     truncation=__snake_case ,     max_length=__snake_case  )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              __magic_name__      				=    datasets.map(
                  __snake_case ,     batched=__snake_case ,     remove_columns=['idx', 'sentence1', 'sentence2'] ,     )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       __magic_name__      				=    tokenized_datasets.rename_column('label' ,     'labels'  )

       def collate_fn(__snake_case: List[Any]  ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              __magic_name__      				=    1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     __magic_name__      				=    1_6
              elif accelerator.mixed_precision != "no":
                     __magic_name__      				=    8
              else:
                     __magic_name__      				=    None

              return tokenizer.pad(
                  __snake_case ,     padding='longest' ,     max_length=__snake_case ,     pad_to_multiple_of=__snake_case ,     return_tensors='pt' ,     )

       # Instantiate dataloaders.
       __magic_name__      				=    DataLoader(
           tokenized_datasets['train'] ,     shuffle=__snake_case ,     collate_fn=__snake_case ,     batch_size=__snake_case  )
       __magic_name__      				=    DataLoader(
           tokenized_datasets['validation'] ,     shuffle=__snake_case ,     collate_fn=__snake_case ,     batch_size=__snake_case  )

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 snake_case : Tuple     		=		mocked_dataloaders  # noqa: F811


def 							A						(				__snake_case: Dict ,     __snake_case: Union[str, Any]  )    ->		Any:

       """simple docstring"""





       if os.environ.get('TESTING_MOCKED_DATALOADERS' ,     __snake_case  ) == "1":
              __magic_name__      				=    2
       # New Code #
       __magic_name__      				=    int(args.gradient_accumulation_steps  )
       # Initialize accelerator
       __magic_name__      				=    Accelerator(
           cpu=args.cpu ,     mixed_precision=args.mixed_precision ,     gradient_accumulation_steps=__snake_case  )
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'  )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       __magic_name__      				=    config['lr']
       __magic_name__      				=    int(config['num_epochs']  )
       __magic_name__      				=    int(config['seed']  )
       __magic_name__      				=    int(config['batch_size']  )

       __magic_name__      				=    evaluate.load('glue' ,     'mrpc'  )

       set_seed(__snake_case  )
       __magic_name__				,  __magic_name__      				=    get_dataloaders(__snake_case ,     __snake_case  )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       __magic_name__      				=    AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,     return_dict=__snake_case  )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       __magic_name__      				=    model.to(accelerator.device  )

       # Instantiate optimizer
       __magic_name__      				=    AdamW(params=model.parameters() ,     lr=__snake_case  )

       # Instantiate scheduler
       __magic_name__      				=    get_linear_schedule_with_warmup(
           optimizer=__snake_case ,     num_warmup_steps=1_0_0 ,     num_training_steps=(len(__snake_case  ) * num_epochs) ,     )

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       __magic_name__				,  __magic_name__				,  __magic_name__				,  __magic_name__				,  __magic_name__      				=    accelerator.prepare(
           __snake_case ,     __snake_case ,     __snake_case ,     __snake_case ,     __snake_case  )

       # Now we train the model
       for epoch in range(__snake_case  ):
              model.train()
              for step, batch in enumerate(__snake_case  ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device  )
                     # New code #
                     # We use the new `accumulate` context manager to perform gradient accumulation
                     # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                     with accelerator.accumulate(__snake_case  ):
                            __magic_name__      				=    model(**__snake_case  )
                            __magic_name__      				=    output.loss
                            accelerator.backward(__snake_case  )
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(__snake_case  ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device  )
                     with torch.no_grad():
                            __magic_name__      				=    model(**__snake_case  )
                     __magic_name__      				=    outputs.logits.argmax(dim=-1  )
                     __magic_name__				,  __magic_name__      				=    accelerator.gather_for_metrics((predictions, batch['labels'])  )
                     metric.add_batch(
                         predictions=__snake_case ,     references=__snake_case ,     )

              __magic_name__      				=    metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:""" ,     __snake_case  )


def 							A						(				)    ->		Optional[int]:

       """simple docstring"""





       __magic_name__      				=    argparse.ArgumentParser(description='Simple example of training script.'  )
       parser.add_argument(
           '--mixed_precision' ,     type=__snake_case ,     default=__snake_case ,     choices=['no', 'fp16', 'bf16', 'fp8'] ,     help='Whether to use mixed precision. Choose'
           'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
           'and an Nvidia Ampere GPU.' ,     )
       # New Code #
       parser.add_argument(
           '--gradient_accumulation_steps' ,     type=__snake_case ,     default=1 ,     help='The number of minibatches to be ran before gradients are accumulated.' ,     )
       parser.add_argument('--cpu' ,     action='store_true' ,     help='If passed, will train on the CPU.'  )
       __magic_name__      				=    parser.parse_args()
       __magic_name__      				=    {'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
       training_function(__snake_case ,     __snake_case  )


if __name__ == "__main__":
 main()







"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 							A						(				__snake_case: Optional[int]  )    ->		Tuple:

       """simple docstring"""





       for param in module.parameters():
              __magic_name__      				=    False


def 							A						(				)    ->		Optional[Any]:

       """simple docstring"""





       __magic_name__      				=    'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              __magic_name__      				=    'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'  )
       return device


def 							A						(				__snake_case: int  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    plt.imshow(__snake_case  )
       fig.axes.get_xaxis().set_visible(__snake_case  )
       fig.axes.get_yaxis().set_visible(__snake_case  )
       plt.show()






def 							A						(				)    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    datetime.now()
       __magic_name__      				=    current_time.strftime('%H:%M:%S'  )
       return timestamp